A Taxonomy of Error Sources in HPC I/O Machine Learning Models

Mihailo Isakov; Mikaela Currier; Eliakin del Rosario; Sandeep Madireddy; Prasanna Balaprakash; Philip Carns; Robert B. Ross; Glenn K. Lockwood; Michel A. Kinsy

arXiv · 2204.08180

A Taxonomy of Error Sources in HPC I/O Machine Learning Models

Mihailo Isakov·Mikaela Currier·Eliakin del Rosario·Sandeep Madireddy·Prasanna Balaprakash·Philip Carns·Robert B. Ross·Glenn K. Lockwood·Michel A. Kinsy

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Abstract

I/O efficiency is crucial to productivity in scientific computing, but the increasing complexity of the system and the applications makes it difficult for practitioners to understand and optimize I/O behavior at scale. Data-driven machine learning-based I/O throughput models offer a solution: they can be used to identify bottlenecks, automate I/O tuning, or optimize job scheduling with minimal human intervention. Unfortunately, current state-of-the-art I/O models are not robust enough for production use and underperform after being deployed. We analyze multiple years of application, scheduler, and storage system logs on two leadership-class HPC platforms to understand why I/O models underperform in practice. We propose a taxonomy consisting of five categories of I/O modeling errors: poor application and system modeling, inadequate dataset coverage, I/O contention, and I/O noise. We develop litmus tests to quantify each category, allowing researchers to narrow down failure modes, enhance I/O throughput models, and improve future generations of HPC logging and analysis tools.

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Mihailo Isakov, Mikaela Currier, Eliakin del Rosario, Sandeep Madireddy, Prasanna Balaprakash, Philip Carns, Robert B. Ross, Glenn K. Lockwood, Michel A. Kinsy. 2022-04-18. A Taxonomy of Error Sources in HPC I/O Machine Learning Models. https://arxiv.org/abs/2204.08180

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